Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA.

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  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BioMed Central, [2001-
    • Subject Terms:
      Bayes Theorem* ; Pandemics* ; Pneumonia, Viral*; Betacoronavirus/*isolation & purification ; Clinical Laboratory Techniques/*methods ; Coronavirus Infections/*diagnosis; Alberta/epidemiology ; Betacoronavirus/pathogenicity ; Bias ; COVID-19 ; COVID-19 Testing ; Clinical Laboratory Techniques/standards ; Coronavirus Infections/epidemiology ; Coronavirus Infections/virology ; Humans ; Philadelphia/epidemiology ; SARS-CoV-2 ; Sensitivity and Specificity ; Uncertainty
    • Abstract:
      Background: Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.
      Methods: We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.
      Results: Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.
      Conclusion: The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.
    • Comments:
      Update of: medRxiv. 2020 Apr 11;:. (PMID: 32511580)
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    • Grant Information:
      K01 AI143356 United States AI NIAID NIH HHS; K01AI143356 International National Institute of Allergy and Infectious Diseases
    • Contributed Indexing:
      Keywords: Bias analysis; Epidemic; Sensitivity; Simulation; Specificity; Uncertainty; Under-diagnosis
    • Publication Date:
      Date Created: 20200608 Date Completed: 20200622 Latest Revision: 20240328
    • Publication Date:
      20240329
    • Accession Number:
      PMC7275354
    • Accession Number:
      10.1186/s12874-020-01037-4
    • Accession Number:
      32505172